399 research outputs found
Evaluation in artificial intelligence: From task-oriented to ability-oriented measurement
The final publication is available at Springer via http://dx.doi.org/ 10.1007/s10462-016-9505-7.The evaluation of artificial intelligence systems and components is crucial for the
progress of the discipline. In this paper we describe and critically assess the different ways
AI systems are evaluated, and the role of components and techniques in these systems. We
first focus on the traditional task-oriented evaluation approach. We identify three kinds of
evaluation: human discrimination, problem benchmarks and peer confrontation. We describe
some of the limitations of the many evaluation schemes and competitions in these three categories,
and follow the progression of some of these tests. We then focus on a less customary
(and challenging) ability-oriented evaluation approach, where a system is characterised by
its (cognitive) abilities, rather than by the tasks it is designed to solve. We discuss several
possibilities: the adaptation of cognitive tests used for humans and animals, the development
of tests derived from algorithmic information theory or more integrated approaches under
the perspective of universal psychometrics. We analyse some evaluation tests from AI that
are better positioned for an ability-oriented evaluation and discuss how their problems and
limitations can possibly be addressed with some of the tools and ideas that appear within
the paper. Finally, we enumerate a series of lessons learnt and generic guidelines to be used
when an AI evaluation scheme is under consideration.I thank the organisers of the AEPIA Summer School On Artificial Intelligence, held in September 2014, for giving me the opportunity to give a lecture on 'AI Evaluation'. This paper was born out of and evolved through that lecture. The information about many benchmarks and competitions discussed in this paper have been contrasted with information from and discussions with many people: M. Bedia, A. Cangelosi, C. Dimitrakakis, I. GarcIa-Varea, Katja Hofmann, W. Langdon, E. Messina, S. Mueller, M. Siebers and C. Soares. Figure 4 is courtesy of F. Martinez-Plumed. Finally, I thank the anonymous reviewers, whose comments have helped to significantly improve the balance and coverage of the paper. This work has been partially supported by the EU (FEDER) and the Spanish MINECO under Grants TIN 2013-45732-C4-1-P, TIN 2015-69175-C4-1-R and by Generalitat Valenciana PROMETEOII2015/013.JosĂ© HernĂĄndez-Orallo (2016). Evaluation in artificial intelligence: From task-oriented to ability-oriented measurement. Artificial Intelligence Review. 1-51. https://doi.org/10.1007/s10462-016-9505-7S151Abel D, Agarwal A, Diaz F, Krishnamurthy A, Schapire RE (2016) Exploratory gradient boosting for reinforcement learning in complex domains. arXiv preprint arXiv:1603.04119Adams S, Arel I, Bach J, Coop R, Furlan R, Goertzel B, Hall JS, Samsonovich A, Scheutz M, Schlesinger M, Shapiro SC, Sowa J (2012) Mapping the landscape of human-level artificial general intelligence. AI Mag 33(1):25â42Adams SS, Banavar G, Campbell M (2016) I-athlon: towards a multi-dimensional Turing test. AI Mag 37(1):78â84AlcalĂĄ J, FernĂĄndez A, Luengo J, Derrac J, GarcĂa S, SĂĄnchez L, Herrera F (2010) Keel data-mining software tool: data set repository, integration of algorithms and experimental analysis framework. J Mult Valued Logic Soft Comput 17:255â287Alexander JRM, Smales S (1997) Intelligence, learning and long-term memory. Personal Individ Differ 23(5):815â825Alpcan T, Everitt T, Hutter M (2014) Can we measure the difficulty of an optimization problem? In: IEEE information theory workshop (ITW)Alur R, Bodik R, Juniwal G, Martin MMK, Raghothaman M, Seshia SA, Singh R, Solar-Lezama A, Torlak E, Udupa A (2013) Syntax-guided synthesis. In: Formal methods in computer-aided design (FMCAD), 2013, IEEE, pp 1â17Alvarado N, Adams SS, Burbeck S, Latta C (2002) Beyond the Turing test: performance metrics for evaluating a computer simulation of the human mind. In: Proceedings of the 2nd international conference on development and learning, IEEE, pp 147â152Amigoni F, Bastianelli E, Berghofer J, Bonarini A, Fontana G, Hochgeschwender N, Iocchi L, Kraetzschmar G, Lima P, Matteucci M, Miraldo P, Nardi D, Schiaffonati V (2015) Competitions for benchmarking: task and functionality scoring complete performance assessment. IEEE Robot Autom Mag 22(3):53â61Anderson J, Lebiere C (2003) The Newell test for a theory of cognition. Behav Brain Sci 26(5):587â601Anderson J, Baltes J, Cheng CT (2011) Robotics competitions as benchmarks for AI research. Knowl Eng Rev 26(01):11â17Arel I, Rose DC, Karnowski TP (2010) Deep machine learningâa new frontier in artificial intelligence research. IEEE Comput Intell Mag 5(4):13â18Asada M, Hosoda K, Kuniyoshi Y, Ishiguro H, Inui T, Yoshikawa Y, Ogino M, Yoshida C (2009) Cognitive developmental robotics: a survey. IEEE Trans Auton Ment Dev 1(1):12â34Aziz H, Brill M, Fischer F, Harrenstein P, Lang J, Seedig HG (2015) Possible and necessary winners of partial tournaments. J Artif Intell Res 54:493â534Bache K, Lichman M (2013) UCI machine learning repository. http://archive.ics.uci.edu/mlBagnall AJ, Zatuchna ZV (2005) On the classification of maze problems. In: Bull L, Kovacs T (eds) Foundations of learning classifier system. Studies in fuzziness and soft computing, vol. 183, Springer, pp 305â316. http://rd.springer.com/chapter/10.1007/11319122_12Baldwin D, Yadav SB (1995) The process of research investigations in artificial intelligence - a unified view. IEEE Trans Syst Man Cybern 25(5):852â861Bellemare MG, Naddaf Y, Veness J, Bowling M (2013) The arcade learning environment: an evaluation platform for general agents. J Artif Intell Res 47:253â279Besold TR (2014) A note on chances and limitations of psychometric ai. In: KI 2014: advances in artificial intelligence. Springer, pp 49â54Biever C (2011) Ultimate IQ: one test to rule them all. New Sci 211(2829, 10 September 2011):42â45Borg M, Johansen SS, Thomsen DL, Kraus M (2012) Practical implementation of a graphics Turing test. In: Advances in visual computing. Springer, pp 305â313Boring EG (1923) Intelligence as the tests test it. New Repub 35â37Bostrom N (2014) Superintelligence: paths, dangers, strategies. Oxford University Press, OxfordBrazdil P, Carrier CG, Soares C, Vilalta R (2008) Metalearning: applications to data mining. Springer, New YorkBringsjord S (2011) Psychometric artificial intelligence. J Exp Theor Artif Intell 23(3):271â277Bringsjord S, Schimanski B (2003) What is artificial intelligence? Psychometric AI as an answer. In: International joint conference on artificial intelligence, pp 887â893Brundage M (2016) Modeling progress in ai. AAAI 2016 Workshop on AI, Ethics, and SocietyBuchanan BG (1988) Artificial intelligence as an experimental science. Springer, New YorkBuhrmester M, Kwang T, Gosling SD (2011) Amazonâs mechanical turk a new source of inexpensive, yet high-quality, data? Perspect Psychol Sci 6(1):3â5Bursztein E, Aigrain J, Moscicki A, Mitchell JC (2014) The end is nigh: generic solving of text-based captchas. In: Proceedings of the 8th USENIX conference on Offensive Technologies, USENIX Association, p 3Campbell M, Hoane AJ, Hsu F (2002) Deep Blue. Artif Intell 134(1â2):57â83Cangelosi A, Schlesinger M, Smith LB (2015) Developmental robotics: from babies to robots. MIT Press, CambridgeCaputo B, MĂŒller H, Martinez-Gomez J, Villegas M, Acar B, Patricia N, Marvasti N, ĂskĂŒdarlı S, Paredes R, Cazorla M et al (2014) Imageclef 2014: overview and analysis of the results. In: Information access evaluation. Multilinguality, multimodality, and interaction, Springer, pp 192â211Carlson A, Betteridge J, Kisiel B, Settles B, Hruschka ER Jr, Mitchell TM (2010) Toward an architecture for never-ending language learning. In: AAAI, vol 5, p 3Carroll JB (1993) Human cognitive abilities: a survey of factor-analytic studies. Cambridge University Press, CambridgeCaruana R (1997) Multitask learning. Mach Learn 28(1):41â75Chaitin GJ (1982) Gödelâs theorem and information. Int J Theor Phys 21(12):941â954Chandrasekaran B (1990) What kind of information processing is intelligence? In: The foundation of artificial intelligenceâa sourcebook. Cambridge University Press, pp 14â46Chater N (1999) The search for simplicity: a fundamental cognitive principle? Q J Exp Psychol Sect A 52(2):273â302Chater N, VitĂĄnyi P (2003) Simplicity: a unifying principle in cognitive science? Trends Cogn Sci 7(1):19â22Chu Z, Gianvecchio S, Wang H, Jajodia S (2010) Who is tweeting on twitter: human, bot, or cyborg? In: Proceedings of the 26th annual computer security applications conference, ACM, pp 21â30Cochran WG (2007) Sampling techniques. Wiley, New YorkCohen PR, Howe AE (1988) How evaluation guides AI research: the message still counts more than the medium. AI Mag 9(4):35Cohen Y (2013) Testing and cognitive enhancement. Technical repor, National Institute for Testing and Evaluation, Jerusalem, IsraelConrad JG, Zeleznikow J (2013) The significance of evaluation in AI and law: a case study re-examining ICAIL proceedings. In: Proceedings of the 14th international conference on artificial intelligence and law, ACM, pp 186â191Conrad JG, Zeleznikow J (2015) The role of evaluation in ai and law. In: Proceedings of the 15th international conference on artificial intelligence and law, pp 181â186Deary IJ, Der G, Ford G (2001) Reaction times and intelligence differences: a population-based cohort study. Intelligence 29(5):389â399Decker KS, Durfee EH, Lesser VR (1989) Evaluating research in cooperative distributed problem solving. Distrib Artif Intell 2:487â519DemĆĄar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1â30Detterman DK (2011) A challenge to Watson. Intelligence 39(2â3):77â78Dimitrakakis C (2016) Personal communicationDimitrakakis C, Li G, Tziortziotis N (2014) The reinforcement learning competition 2014. AI Mag 35(3):61â65Dowe DL (2013) Introduction to Ray Solomonoff 85th memorial conference. In: Dowe DL (ed) Algorithmic probability and friends. Bayesian prediction and artificial intelligence, lecture notes in computer science, vol 7070. Springer, Berlin, pp 1â36Dowe DL, Hajek AR (1997) A computational extension to the Turing Test. In: Proceedings of the 4th conference of the Australasian cognitive science society, University of Newcastle, NSW, AustraliaDowe DL, Hajek AR (1998) A non-behavioural, computational extension to the Turing test. In: International conference on computational intelligence and multimedia applications (ICCIMAâ98), Gippsland, Australia, pp 101â106Dowe DL, HernĂĄndez-Orallo J (2012) IQ tests are not for machines, yet. Intelligence 40(2):77â81Dowe DL, HernĂĄndez-Orallo J (2014) How universal can an intelligence test be? Adapt Behav 22(1):51â69Drummond C (2009) Replicability is not reproducibility: nor is it good science. In: Proceedings of the evaluation methods for machine learning workshop at the 26th ICML, Montreal, CanadaDrummond C, Japkowicz N (2010) Warning: statistical benchmarking is addictive. Kicking the habit in machine learning. J Exp Theor Artif Intell 22(1):67â80Duan Y, Chen X, Houthooft R, Schulman J, Abbeel P (2016) Benchmarking deep reinforcement learning for continuous control. arXiv preprint arXiv:1604.06778Eden AH, Moor JH, Soraker JH, Steinhart E (2013) Singularity hypotheses: a scientific and philosophical assessment. Springer, New YorkEdmondson W (2012) The intelligence in ETIâwhat can we know? Acta Astronaut 78:37â42Elo AE (1978) The rating of chessplayers, past and present, vol 3. Batsford, LondonEmbretson SE, Reise SP (2000) Item response theory for psychologists. L. Erlbaum, HillsdaleEvans JM, Messina ER (2001) Performance metrics for intelligent systems. NIST Special Publication SP, pp 101â104Everitt T, Lattimore T, Hutter M (2014) Free lunch for optimisation under the universal distribution. In: 2014 IEEE Congress on evolutionary computation (CEC), IEEE, pp 167â174Falkenauer E (1998) On method overfitting. J Heuristics 4(3):281â287Feldman J (2003) Simplicity and complexity in human concept learning. Gen Psychol 38(1):9â15Ferrando PJ (2009) Difficulty, discrimination, and information indices in the linear factor analysis model for continuous item responses. Appl Psychol Meas 33(1):9â24Ferrando PJ (2012) Assessing the discriminating power of item and test scores in the linear factor-analysis model. PsicolĂłgica 33:111â139Ferri C, HernĂĄndez-Orallo J, Modroiu R (2009) An experimental comparison of performance measures for classification. Pattern Recogn Lett 30(1):27â38Ferrucci D, Brown E, Chu-Carroll J, Fan J, Gondek D, Kalyanpur AA, Lally A, Murdock J, Nyberg E, Prager J et al (2010) Building Watson: an overview of the DeepQA project. AI Mag 31(3):59â79Fogel DB (1991) The evolution of intelligent decision making in gaming. Cybern Syst 22(2):223â236Gaschnig J, Klahr P, Pople H, Shortliffe E, Terry A (1983) Evaluation of expert systems: issues and case studies. Build Exp Syst 1:241â278Geissman JR, Schultz RD (1988) Verification & validation. AI Exp 3(2):26â33Genesereth M, Love N, Pell B (2005) General game playing: overview of the AAAI competition. AI Mag 26(2):62GerĂłnimo D, LĂłpez AM (2014) Datasets and benchmarking. In: Vision-based pedestrian protection systems for intelligent vehicles. Springer, pp 87â93Goertzel B, Pennachin C (eds) (2007) Artificial general intelligence. Springer, New YorkGoertzel B, Arel I, Scheutz M (2009) Toward a roadmap for human-level artificial general intelligence: embedding HLAI systems in broad, approachable, physical or virtual contexts. Artif Gen Intell Roadmap InitiatGoldreich O, Vadhan S (2007) Special issue on worst-case versus average-case complexity editorsâ foreword. Comput complex 16(4):325â330Gordon BB (2007) Report on panel discussion on (re-)establishing or increasing collaborative links between artificial intelligence and intelligent systems. In: Messina ER, Madhavan R (eds) Proceedings of the 2007 workshop on performance metrics for intelligent systems, pp 302â303Gulwani S, HernĂĄndez-Orallo J, Kitzelmann E, Muggleton SH, Schmid U, Zorn B (2015) Inductive programming meets the real world. Commun ACM 58(11):90â99Hand DJ (2004) Measurement theory and practice. A Hodder Arnold Publication, LondonHernĂĄndez-Orallo J (2000a) Beyond the Turing test. J Logic Lang Inf 9(4):447â466HernĂĄndez-Orallo J (2000b) On the computational measurement of intelligence factors. In: Meystel A (ed) Performance metrics for intelligent systems workshop. National Institute of Standards and Technology, Gaithersburg, pp 1â8HernĂĄndez-Orallo J (2000c) Thesis: computational measures of information gain and reinforcement in inference processes. AI Commun 13(1):49â50HernĂĄndez-Orallo J (2010) A (hopefully) non-biased universal environment class for measuring intelligence of biological and artificial systems. In: Artificial general intelligence, 3rd International Conference. Atlantis Press, Extended report at http://users.dsic.upv.es/proy/anynt/unbiased.pdf , pp 182â183HernĂĄndez-Orallo J (2014) On environment difficulty and discriminating power. Auton Agents Multi-Agent Syst. 29(3):402â454. doi: 10.1007/s10458-014-9257-1HernĂĄndez-Orallo J, Dowe DL (2010) Measuring universal intelligence: towards an anytime intelligence test. Artif Intell 174(18):1508â1539HernĂĄndez-Orallo J, Dowe DL (2013) On potential cognitive abilities in the machine kingdom. Minds Mach 23:179â210HernĂĄndez-Orallo J, Minaya-Collado N (1998) A formal definition of intelligence based on an intensional variant of Kolmogorov complexity. In: Proceedings of international symposium of engineering of intelligent systems (EISâ98), ICSC Press, pp 146â163HernĂĄndez-Orallo J, Dowe DL, España-Cubillo S, HernĂĄndez-Lloreda MV, Insa-Cabrera J (2011) On more realistic environment distributions for defining, evaluating and developing intelligence. In: Schmidhuber J, ThĂłrisson K, Looks M (eds) Artificial general intelligence, LNAI, vol 6830. Springer, New York, pp 82â91HernĂĄndez-Orallo J, Flach P, Ferri C (2012a) A unified view of performance metrics: translating threshold choice into expected classification loss. J Mach Learn Res 13(1):2813â2869HernĂĄndez-Orallo J, Insa-Cabrera J, Dowe DL, Hibbard B (2012b) Turing Tests with Turing machines. In: Voronkov A (ed) Turing-100, EPiC Series, vol 10, pp 140â156HernĂĄndez-Orallo J, Dowe DL, HernĂĄndez-Lloreda MV (2014) Universal psychometrics: measuring cognitive abilities in the machine kingdom. Cogn Syst Res 27:50â74HernĂĄndez-Orallo J, MartĂnez-Plumed F, Schmid U, Siebers M, Dowe DL (2016) Computer models solving intelligence test problems: progress and implications. Artif Intell 230:74â107Herrmann E, Call J, HernĂĄndez-Lloreda MV, Hare B, Tomasello M (2007) Humans have evolved specialized skills of social cognition: the cultural intelligence hypothesis. Science 317(5843):1360â1366Hibbard B (2009) Bias and no free lunch in formal measures of intelligence. J Artif Gen Intell 1(1):54â61Hingston P (2010) A new design for a Turing Test for bots. In: 2010 IEEE symposium on computational intelligence and games (CIG), IEEE, pp 345â350Hingston P (2012) Believable bots: can computers play like people?. Springer, New YorkHo TK, Basu M (2002) Complexity measures of supervised classification problems. IEEE Trans Pattern Anal Mach Intell 24(3):289â300Hutter M (2007) Universal algorithmic intelligence: a mathematical top â down approach. In: Goertzel B, Pennachin C (eds) Artificial general intelligence, cognitive technologies. Springer, Berlin, pp 227â290Igel C, Toussaint M (2005) A no-free-lunch theorem for non-uniform distributions of target functions. J Math Model Algorithms 3(4):313â322Insa-Cabrera J (2016) Towards a universal test of social intelligence. Ph.D. thesis, Departament de Sistemes InformĂĄtics i ComputaciĂł, UPVInsa-Cabrera J, Dowe DL, España-Cubillo S, HernĂĄndez-Lloreda MV, HernĂĄndez-Orallo J (2011a) Comparing humans and ai agents. In: Schmidhuber J, ThĂłrisson K, Looks M (eds) Artificial general intelligence, LNAI, vol 6830. Springer, New York, pp 122â132Insa-Cabrera J, Dowe DL, HernĂĄndez-Orallo J (2011) Evaluating a reinforcement learning algorithm with a general intelligence test. In: Lozano JA, Gamez JM (eds) Current topics in artificial intelligence. CAEPIA 2011, LNAI series 7023. Springer, New YorkInsa-Cabrera J, Benacloch-Ayuso JL, HernĂĄndez-Orallo J (2012) On measuring social intelligence: experiments on competition and cooperation. In: Bach J, Goertzel B, IklĂ© M (eds) AGI, lecture notes in computer science, vol 7716. Springer, New York, pp 126â135Jacoff A, Messina E, Weiss BA, Tadokoro S, Nakagawa Y (2003) Test arenas and performance metrics for urban search and rescue robots. In: Proceedings of 2003 IEEE/RSJ international conference on intelligent robots and systems, 2003 (IROS 2003), IEEE, vol 4, pp 3396â3403Japkowicz N, Shah M (2011) Evaluating learning algorithms. Cambridge University Press, CambridgeJiang J (2008) A literature survey on domain adaptation of statistical classifiers. http://sifaka.cs.uiuc.edu/jiang4/domain_adaptation/surveyJohnson M, Hofmann K, Hutton T, Bignell D (2016) The Malmo platform for artificial intelligence experimentation. In: International joint conference on artificial intelligence (IJCAI)Keith TZ, Reynolds MR (2010) CattellâHornâCarroll abilities and cognitive tests: what weâve learned from 20 years of research. Psychol Schools 47(7):635â650Ketter W, Symeonidis A (2012) Competitive benchmarking: lessons learned from the trading agent competition. AI Mag 33(2):103Khreich W, Granger E, Miri A, Sabourin R (2012) A survey of techniques for incremental learning of HMM parameters. Inf Sci 197:105â130Kim JH (2004) Soccer robotics, vol 11. Springer, New YorkKitano H, Asada M, Kuniyoshi Y, Noda I, Osawa E (1997) Robocup: the robot world cup initiative. In: Proceedings of the first international conference on autonomous agents, ACM, pp 340â347Kleiner K (2011) Who are you calling bird-brained? An attempt is being made to devise a universal intelligence test. Economist 398(8723, 5 March 2011):82Knuth DE (1973) Sorting and searching, volume 3 of the art of computer programming. Addison-Wesley, ReadingKoza JR (2010) Human-competitive results produced by genetic programming. Genet Program Evolvable Mach 11(3â4):251â284Krueger J, Osherson D (1980) On the psychology of structural simplicity. In: Jusczyk PW, Klein RM (eds) The nature of thought: essays in honor of D. O. Hebb. Psychology Press, London, pp 187â205Langford J (2005) Clever methods of overfitting. Machine Learning (Theory). http://hunch.netLangley P (1987) Research papers in machine learning. Mach Learn 2(3):195â198Langley P (2011) The changing science of machine learning. Mach Learn 82(3):275â279Langley P (2012) The cognitive systems paradigm. Adv Cogn Syst 1:3â13Lattimore T, Hutter M (2013) No free lunch versus Occamâs razor in supervised learning. Algorithmic Probability and Friends. Springer, Bayesian Prediction and Artificial Intelligence, pp 223â235Leeuwenberg ELJ, Van Der Helm PA (2012) Structural information theory: the simplicity of visual form. Cambridge University Press, CambridgeLegg S, Hutter M (2007a) Tests of machine intelligence. In: Lungarella M, Iida F, Bongard J, Pfeifer R (eds) 50 Years of Artificial Intelligence, Lecture Notes in Computer Science, vol 4850, Springer Berlin Heidelberg, pp 232â242. doi: 10.1007/978-3-540-77296-5_22Legg S, Hutter M (2007b) Universal intelligence: a definition of machine intelligence. Minds Mach 17(4):391â444Legg S, Veness J (2013) An approximation of the universal intelligence measure. Algorithmic Probability and Friends. Springer, Bayesian Prediction and Artificial Intelligence, pp 236â249Levesque HJ (2014) On our best behaviour. Artif Intell 212:27â35Levesque HJ, Davis E, Morgenstern L (2012) The winog
Observation of Cabibbo-suppressed two-body hadronic decays and precision mass measurement of the baryon
The first observation of the singly Cabibbo-suppressed
and decays
is reported, using proton-proton collision data at a centre-of-mass energy of
, corresponding to an integrated luminosity of , collected with the LHCb detector between 2016 and 2018. The
branching fraction ratios are measured to be
,
. In addition, using the
decay channel, the baryon
mass is measured to be , improving the
precision of the previous world average by a factor of four.Comment: All figures and tables, along with any supplementary material and
additional information, are available at
https://cern.ch/lhcbproject/Publications/p/LHCb-PAPER-2023-011.html (LHCb
public pages
Measurement of boson production cross-section in collisions at TeV
The first measurement of the boson production cross-section at
centre-of-mass energy TeV in the forward region is reported,
using collision data collected by the LHCb experiment in year 2017,
corresponding to an integrated luminosity of . The
production cross-section is measured for final-state muons in the
pseudorapidity range . The integrated cross-section is determined to be for the di-muon invariant
mass in the range . This result and the
differential cross-section results are in good agreement with theoretical
predictions at next-to-next-to-leading order in the strong coupling.
Based on a previous LHCb measurement of the boson production
cross-section in Pb collisions at TeV, the nuclear
modification factor is measured for the first time at this
energy. The measured values are in the forward region () and
in the backward region
(), where represents the muon rapidity in
the centre-of-mass frame.Comment: All figures and tables, along with any supplementary material and
additional information, are available at
https://cern.ch/lhcbproject/Publications/p/LHCb-PAPER-2023-010.html (LHCb
public pages
Studies of and production in and Pb collisions
The production of and mesons is studied in proton-proton and
proton-lead collisions collected with the LHCb detector. Proton-proton
collisions are studied at center-of-mass energies of and ,
and proton-lead collisions are studied at a center-of-mass energy per nucleon
of . The studies are performed in center-of-mass rapidity
regions (forward rapidity) and
(backward rapidity) defined relative to the proton beam direction. The
and production cross sections are measured differentially as a function
of transverse momentum for and , respectively. The differential cross sections are used to
calculate nuclear modification factors. The nuclear modification factors for
and mesons agree at both forward and backward rapidity, showing
no significant evidence of mass dependence. The differential cross sections of
mesons are also used to calculate cross section ratios,
which show evidence of a deviation from the world average. These studies offer
new constraints on mass-dependent nuclear effects in heavy-ion collisions, as
well as and meson fragmentation.Comment: All figures and tables, along with machine-readable versions and any
supplementary material and additional information, are available at
https://lhcbproject.web.cern.ch/Publications/p/LHCb-PAPER-2023-030.html (LHCb
public pages
Amplitude analysis of the Îb0âpKâÎł decay
The resonant structure of the radiative decay Îb0âpKâÎł in the region of proton-kaon invariant-mass up to 2.5 GeV/c2 is studied using proton-proton collision data recorded at centre-of-mass energies of 7, 8, and 13 TeV collected with the LHCb detector, corresponding to a total integrated luminosity of 9 fbâ1. Results are given in terms of fit and interference fractions between the different components contributing to this final state. Only Î resonances decaying to pKâ are found to be relevant, where the largest contributions stem from the Î(1520), Î(1600), Î(1800), and Î(1890) states
Study of charmonium decays to in the channels
A study of the and decays
is performed using proton-proton collisions at center-of-mass energies of 7, 8
and 13 TeV at the LHCb experiment. The invariant mass spectra from
both decay modes reveal a rich content of charmonium resonances. New precise
measurements of the and resonance parameters are
performed and branching fraction measurements are obtained for decays to
, , and resonances. In particular, the
first observation and branching fraction measurement of is reported as well as first measurements of the
and branching fractions. Dalitz plot analyses of
and decays are performed. A
new measurement of the amplitude and phase of the -wave as functions
of the mass is performed, together with measurements of the
, and parameters. Finally, the branching
fractions of decays to resonances are also measured.Comment: All figures and tables, along with any supplementary material and
additional information, are available at
https://cern.ch/lhcbproject/Publications/p/LHCb-PAPER-2022-051.html (LHCb
public pages
Observation of the decays
This paper reports the observation of the decays using proton-proton collision data collected by the
LHCb experiment, corresponding to an integrated luminosity of
. The branching fractions of these decays are measured
relative to the normalisation channel .
The meson is reconstructed in the
decay channel and the products of branching
fractions are measured to be The first uncertainty is
statistical, the second systematic, and the third arises from the uncertainty
of the branching fraction of the
normalisation channel. The last uncertainty in the result is due to
the limited knowledge of the fragmentation fraction ratio, . The
significance for the and signals is larger than
. The ratio of the helicity amplitudes which governs the angular
distribution of the decay
is determined from the data. The ratio of the - and -wave amplitudes is
found to be and its phase rad,
where the first uncertainty is statistical and the second systematic.Comment: All figures and tables, along with machine-readable versions and any
supplementary material and additional information, are available at
https://cern.ch/lhcbproject/Publications/p/LHCb-PAPER-2023-014.html (LHCb
public pages
Fraction of decays in prompt production measured in pPb collisions at TeV
The fraction of and decays in the prompt
yield, , is measured by
the LHCb detector in pPb collisions at TeV. The study
covers the forward () and backward () rapidity
regions, where is the rapidity in the nucleon-nucleon
center-of-mass system. Forward and backward rapidity samples correspond to
integrated luminosities of 13.6 0.3 nb and 20.8 0.5
nb, respectively. The result is presented as a function of the
transverse momentum in the range 1 GeV/.
The fraction at forward rapidity is compatible with the LHCb
measurement performed in collisions at TeV, whereas the
result at backward rapidity is 2.4 larger than in the forward region
for GeV/. The increase of at low at backward rapidity is compatible with the suppression of the
(2S) contribution to the prompt yield. The lack of in-medium
dissociation of states observed in this study sets an upper limit of
180 MeV on the free energy available in these pPb collisions to dissociate or
inhibit charmonium state formation.Comment: All figures and tables, along with machine-readable versions and any
supplementary material and additional information, are available at
https://cern.ch/lhcbproject/Publications/p/LHCb-PAPER-2023-028.html (LHCb
public pages
Enhanced production of baryons in high-multiplicity collisions at TeV
The production rate of baryons relative to mesons
in collisions at a center-of-mass energy TeV is measured
by the LHCb experiment. The ratio of to production
cross-sections shows a significant dependence on both the transverse momentum
and the measured charged-particle multiplicity. At low multiplicity, the ratio
measured at LHCb is consistent with the value measured in
collisions, and increases by a factor of with increasing multiplicity.
At relatively low transverse momentum, the ratio of to
cross-sections is higher than what is measured in
collisions, but converges with the ratio as the momentum
increases. These results imply that the evolution of heavy quarks into
final-state hadrons is influenced by the density of the hadronic environment
produced in the collision. Comparisons with a statistical hadronization model
and implications for the mechanisms enforcing quark confinement are discussed.Comment: All figures and tables, along with machine-readable versions and any
supplementary material and additional information, are available at
https://cern.ch/lhcbproject/Publications/p/LHCb-PAPER-2023-027.html (LHCb
public pages
A measurement of
Using a dataset corresponding to of integrated
luminosity collected with the LHCb detector between 2011 and 2018 in
proton-proton collisions, the decay-time distributions of the decay modes
and
are studied. The decay-width difference between the light and heavy mass
eigenstates of the meson is measured to be , where the first uncertainty is
statistical and the second systematic.Comment: All figures and tables, along with machine-readable versions and any
supplementary material and additional information, are available at
https://cern.ch/lhcbproject/Publications/p/LHCb-PAPER-2023-025.htm
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